An epsilon-based data envelopment analysis approach for solving performance measurement problems with interval and ordinal dual-role factors

Bohlool Ebrahimi (), Madjid Tavana (), Andreas Kleine () and Andreas Dellnitz ()
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Bohlool Ebrahimi: FernUniversität in Hagen
Madjid Tavana: La Salle University
Andreas Kleine: FernUniversität in Hagen
Andreas Dellnitz: Leibniz-Fachhochschule School of Business

OR Spectrum: Quantitative Approaches in Management, 2021, vol. 43, issue 4, No 9, 1103-1124

Abstract: Abstract Data envelopment analysis (DEA) is a linear programming method for measuring the performance and efficiency of units called decision-making units (DMUs). In many real-world performance measurement problems, the input and output data are not precisely known. Furthermore, the data may include dual-role factors that can be considered an input and output factor simultaneously. We propose a novel DEA model in the presence of imprecise data and imprecise dual-role factors by developing a new pair of mixed binary linear epsilon-based DEA models. The proposed models estimate the lower and upper bound efficiency scores in the presence of interval input, output, and dual-role factors by considering a fixed and unified production frontier for all DMUs. We then extend our models by including the weak ordinal dual-role factors. In contrast to the existing methods that exclude the dual-role factors, we include the dual-role factors and find a strictly positive value for the lower bound of the weights of inputs, outputs, and dual-role factors. We present a case study to demonstrate the applicability and exhibit the superiority of our approach over the existing methods.

Keywords: Data envelopment analysis; Uncertainty; Imprecise data; Dual-role factors; Space satellite industry (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00291-021-00649-6

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